Concept: A priori and a posteriori
The Northwest India Aquifer (NWIA) has been shown to have the highest groundwater depletion (GWD) rate globally, threatening crop production and sustainability of groundwater resources. Gravity Recovery and Climate Experiment (GRACE) satellites have been emerging as a powerful tool to evaluate GWD with ancillary data. Accurate GWD estimation is, however, challenging because of uncertainties in GRACE data processing. We evaluated GWD rates over the NWIA using a variety of approaches, including newly developed constrained forward modeling resulting in a GWD rate of 3.1 ± 0.1 cm/a (or 14 ± 0.4 km(3)/a) for Jan 2005-Dec 2010, consistent with the GWD rate (2.8 cm/a or 12.3 km(3)/a) from groundwater-level monitoring data. Published studies (e.g., 4 ± 1 cm/a or 18 ± 4.4 km(3)/a) may overestimate GWD over this region. This study highlights uncertainties in GWD estimates and the importance of incorporating a priori information to refine spatial patterns of GRACE signals that could be more useful in groundwater resource management and need to be paid more attention in future studies.
Two large cardiovascular outcome trials of canagliflozin, the CANVAS Program, will complete in early 2017: the CANagliflozin cardioVascular Assessment Study (CANVAS) and the CANagliflozin cardioVascular Assessment Study - Renal (CANVAS-R). Accruing data for the sodium-glucose co-transporter 2 inhibitor (SGLT2i) class has identified questions and opportunities that were not apparent when the trials were designed. Accordingly, a series of modifications have been made to the planned analyses. These updates will ensure that the data from the CANVAS Program will maximise advances in scientific knowledge and patient care. The specification of the analysis strategy prior to knowledge of the trial results, their design by the independent scientific trial Steering Committee, the detailed a priori definition of the analysis plans and the external review provided by the US Food and Drug Administration, all provide for a maximally efficient and robust utilisation of the data. The CANVAS Program should significantly advance our understanding of the effects of canagliflozin, and the broader SGLT2i class, on a range of important efficacy and safety outcomes.
Metagenomic shotgun sequencing (MSS) is an important tool for characterizing viral populations. It is culture-independent, requires no a priori knowledge of the viruses in the sample, and may provide useful genomic information. However, MSS can lack sensitivity and may yield insufficient data for detailed analysis. We have created a targeted sequence capture panel, ViroCap, designed to enrich nucleic acid from DNA and RNA viruses from 34 families that infect vertebrate hosts. A computational approach condensed nearly 1 billion base pair (bp) of viral reference sequence into less than 200 million bp of unique, representative sequence suitable for targeted sequence capture. We compared the effectiveness of detecting viruses in standard MSS versus MSS following targeted sequence capture. First, we analyzed two sets of samples, one derived from samples submitted to a diagnostic virology laboratory and one derived from samples collected in a study of fever in children. We detected 14 and 18 viruses in the two sets, comprising 19 genera from 10 families, with dramatic enhancement of genome representation following capture enrichment. The median fold-increases in percent viral reads post-capture were 674 and 296. Median breadth of coverage increased from 2.1% to 83.2% post-capture in the first set and from 2.0% to 75.6% in the second set. Next, we analyzed samples containing a set of diverse anellovirus sequences and demonstrated that ViroCap could be used to detect viral sequences with up to 58% variation from the references used to select capture probes. ViroCap substantially enhances MSS for a comprehensive set of viruses and has utility for research and clinical applications.
The retreating Pine Island Glacier (PIG), West Antarctica, presently contributes ~5-10% of global sea-level rise. PIG’s retreat rate has increased in recent decades with associated thinning migrating upstream into tributaries feeding the main glacier trunk. To project future change requires modelling that includes robust parameterisation of basal traction, the resistance to ice flow at the bed. However, most ice-sheet models estimate basal traction from satellite-derived surface velocity, without a priori knowledge of the key processes from which it is derived, namely friction at the ice-bed interface and form drag, and the resistance to ice flow that arises as ice deforms to negotiate bed topography. Here, we present high-resolution maps, acquired using ice-penetrating radar, of the bed topography across parts of PIG. Contrary to lower-resolution data currently used for ice-sheet models, these data show a contrasting topography across the ice-bed interface. We show that these diverse subglacial landscapes have an impact on ice flow, and present a challenge for modelling ice-sheet evolution and projecting global sea-level rise from ice-sheet loss.
In contrast to conventional multipixel cameras, single-pixel cameras capture images using a single detector that measures the correlations between the scene and a set of patterns. However, these systems typically exhibit low frame rates, because to fully sample a scene in this way requires at least the same number of correlation measurements as the number of pixels in the reconstructed image. To mitigate this, a range of compressive sensing techniques have been developed which use a priori knowledge to reconstruct images from an undersampled measurement set. Here, we take a different approach and adopt a strategy inspired by the foveated vision found in the animal kingdom-a framework that exploits the spatiotemporal redundancy of many dynamic scenes. In our system, a high-resolution foveal region tracks motion within the scene, yet unlike a simple zoom, every frame delivers new spatial information from across the entire field of view. This strategy rapidly records the detail of quickly changing features in the scene while simultaneously accumulating detail of more slowly evolving regions over several consecutive frames. This architecture provides video streams in which both the resolution and exposure time spatially vary and adapt dynamically in response to the evolution of the scene. The degree of local frame rate enhancement is scene-dependent, but here, we demonstrate a factor of 4, thereby helping to mitigate one of the main drawbacks of single-pixel imaging techniques. The methods described here complement existing compressive sensing approaches and may be applied to enhance computational imagers that rely on sequential correlation measurements.
There is a long history of archaeologists and forensic scientists using pollen found in a dust sample to identify its geographic origin or history. Such palynological approaches have important limitations as they require time-consuming identification of pollen grains, a priori knowledge of plant species distributions, and a sufficient diversity of pollen types to permit spatial or temporal identification. We demonstrate an alternative approach based on DNA sequencing analyses of the fungal diversity found in dust samples. Using nearly 1,000 dust samples collected from across the continental U.S., our analyses identify up to 40,000 fungal taxa from these samples, many of which exhibit a high degree of geographic endemism. We develop a statistical learning algorithm via discriminant analysis that exploits this geographic endemicity in the fungal diversity to correctly identify samples to within a few hundred kilometers of their geographic origin with high probability. In addition, our statistical approach provides a measure of certainty for each prediction, in contrast with current palynology methods that are almost always based on expert opinion and devoid of statistical inference. Fungal taxa found in dust samples can therefore be used to identify the origin of that dust and, more importantly, we can quantify our degree of certainty that a sample originated in a particular place. This work opens up a new approach to forensic biology that could be used by scientists to identify the origin of dust or soil samples found on objects, clothing, or archaeological artifacts.
Cyberbullying, a modern form of bullying performed using electronic forms of contact (e.g., SMS, MMS, Facebook, YouTube), has been considered as being worse than traditional bullying in its consequences for the victim. This difference was mainly attributed to some specific aspect that are believed to distinguish cyberbullying from traditional bullying: an increased potential for a large audience, an increased potential for anonymous bullying, lower levels of direct feedback, decreased time and space limits, and lower levels of supervision. The present studies investigated the relative importance of medium (traditional vs. cyber), publicity (public vs. private), and bully’s anonymity (anonymous vs. not anonymous) for the perceived severity of hypothetical bullying scenarios among a sample of Swiss seventh- and eight-graders (study 1: 49 % female, mean age = 13.7; study 2: 49 % female, mean age = 14.2). Participants ranked a set of hypothetical bullying scenarios from the most severe one to the least severe one. The scenarios were experimentally manipulated based on the aspect of medium and publicity (study 1), and medium and anonymity (study 2). Results showed that public scenarios were perceived as worse than private ones, and that anonymous scenarios were perceived as worse than not anonymous ones. Cyber scenarios generally were perceived as worse than traditional ones, although effect sizes were found to be small. These results suggest that the role of medium is secondary to the role of publicity and anonymity when it comes to evaluating bullying severity. Therefore, cyberbullying is not a priori perceived as worse than traditional bullying. Implications of the results for cyberbullying prevention and intervention are discussed.
Event related potentials (ERPs) represent a noninvasive and widely available means to analyze neural correlates of sensory and cognitive processing. Recent developments in neural and cognitive engineering proposed completely new application fields of this well-established measurement technique when using an advanced single-trial processing. We have recently shown that 2-D diffusion filtering methods from image processing can be used for the denoising of ERP single-trials in matrix representations, also called ERP images. In contrast to conventional 1-D transient ERP denoising techniques, the 2-D restoration of ERP images allows for an integration of regularities over multiple stimulations into the denoising process. Advanced anisotropic image restoration methods may require directional information for the ERP denoising process. This is especially true if there is a lack of a priori knowledge about possible traces in ERP images. However due to the use of event related experimental paradigms, ERP images are characterized by a high degree of self-similarity over the individual trials. In this paper, we propose the simple and easy to apply nonlocal means method for ERP image denoising in order to exploit this self-similarity rather than focusing on the edge-based extraction of directional information. Using measured and simulated ERP data, we compare our method to conventional approaches in ERP denoising. It is concluded that the self-similarity in ERP images can be exploited for single-trial ERP denoising by the proposed approach. This method might be promising for a variety of evoked and event-related potential applications, including nonstationary paradigms such as changing exogeneous stimulus characteristics or endogenous states during the experiment. As presented, the proposed approach is for the a posteriori denoising of single-trial sequences.
We propose a new molecular dynamics (MD) protocol to identify the binding site of a guest within a host. The method utilizes a four spatial (4D) dimension representation of the ligand allowing for rapid and efficient sampling within the receptor. We applied the method to two different model receptors characterized by diverse structural features of the binding site and different ligand binding affinities. The Abl kinase domain is comprised of a deep binding pocket and displays high affinity for the two chosen ligands examined here. The PDZ1 domain of PSD-95 has a shallow binding pocket that accommodates a peptide ligand involving far fewer interactions and a micromolar-affinity. To insure a completely unbiased searching, the ligands were placed in the direct center of the protein receptors, away from the binding site, at the start of the 4D MD protocol. In both cases the ligands were successfully docked into the binding site as identified in the published structures. The 4D MD protocol is able to overcome local energy barriers in locating the lowest energy binding pocket and will aid in the discovery of guest binding pockets in the absence of a priori knowledge of the site of interaction.
It is known in thin-film deposition that the density of nucleated clusters N varies with the deposition rate F as a power law, N ∼ F (α). The exponent α is a function of the critical nucleus size i in a way that changes with the aggregation limiting process. We extend here the derivation of the analytical capture-zone distribution function P β(s) = a ß ·s (β) ·exp(-b β s (2)) of Pimpinelli and Einstein to generic aggregation-limiting processes. We show that the parameter β is generally related to the critical nucleus size i and to the exponent α by the equality α·β = i, in the case of compact islands. This remarkable result allows one to measure i with no a priori knowledge of the actual aggregation mechanism. We apply this equality to measuring the critical nucleus size for pentacene deposition on mica. This system shows a crossover from diffusion-limited to attachment-limited aggregation with increasing deposition rates.